Protecting Retail Profits With Artificial Intelligence Posted on : Jan 29 - 2019

Consumer spending is the primary driver of economic growth in the U.S., according to The Wall Street Journal (paywall). This makes every fluctuation in retail sales important to a myriad of businesses. Manufacturers, suppliers and logistics companies, as well as all the industries that support them, benefit from strong retail profits. Retailers are now turning to artificial intelligence to help protect profits and improve the customer experience.

As a chief data scientist, I'm in charge of the development of analytics and artificial intelligence (AI) models used internationally in my company’s retail, health and safety divisions. I believe stores and their e-commerce counterparts should use AI to prune away actions and processes that do not contribute to net sales or customer satisfaction. This is the growing retailer’s best chance to compete against mega-retailers such as Amazon that have a well-developed AI strategy.

What qualifies as AI for profit protection?

While a robotic security guard might help some retailers with surveillance, I am talking about a broader application of AI: It could help protect retail profits by using advanced analytics to develop models that are then employed by a computer to analyze data from a broad range of sources. As the models are used, the outcomes are fed back into them, which incorporates the information in future actions or decisions; the system learns and adapts without human intervention. It makes simple decisions in real time. A few examples of AI include machine learning, deep learning and natural language processing. The largest retailers have used AI for many years to add efficiencies to their businesses in a variety of ways, including marketing, pricing, logistics, risk management, store management, fraud detection, etc.

Turn your focus to efficiency and revenue generation.

In a store, AI might make any number of routine determinations based on data. It can cut through complexity to determine a fair and impartial answer to a process, such as accepting a consumer’s refund request, allocating resources or identifying stocking errors. At the company headquarters, it can be used to find under-performing stores and help determine which steps to take to improve profitability.

I often look for ways that AI can replace repetitive, individual-driven analysis for a retailer. Instead of having 10, 100 or 1,000 people performing the same analysis each day, AI can deliver answers immediately for each end user specific to his or her job role. Not only is this far more efficient, but it ensures consistency across the retailer’s stores; the same data and criteria are applied impartially. View More